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$0 - Bulk loads gff3 files into a chado database.


% $0 [options]
% cat <gff-file> | $0 [options]


--gfffile The file containing GFF3 (optional, can read
from stdin)
--fastafile Fasta file to load sequence from
--organism The organism for the data
(use the value 'fromdata' to read from GFF organism=xxx)
--dbprofile Database config profile name
--dbname Database name
--dbuser Database user name
--dbpass Database password
--dbhost Database host
--dbport Database port
--analysis The GFF data is from computational analysis
--noload Create bulk load files, but don't actually load them.
--nosequence Don't load sequence even if it is in the file
--notransact Don't use a single transaction to load the database
--drop_indexes Drop indexes of affected tables before starting load
and recreate after load is finished; generally
does not help performance.
--validate Validate SOFA terms before attempting insert (can
cause script startup to be slow, off by default)
--ontology Give directions for handling misc Ontology_terms
--skip_vacuum Skip vacuuming the tables after the inserts (default)
--no_skip_vaccum Don't skip vacuuming the tables
--inserts Print INSERT statements instead of COPY FROM STDIN
--noexon Don't convert CDS features to exons (but still create
polypeptide features)
--recreate_cache Causes the uniquename cache to be recreated
--remove_lock Remove the lock to allow a new process to run
--save_tmpfiles Save the temp files used for loading the database
--random_tmp_dir Use a randomly generated tmp dir (the default is
to use the current directory)
--no_target_syn By default, the loader adds the targetId in
the synonyms list of the feature. This flag
deactivate this.
--unique_target Trust the unicity of the target IDs. IDs are case
sensitive. By default, the uniquename of a new target
will be 'TargetId_PrimaryKey'. With this flag,
it will be 'TargetId'. Furthermore, the Name of the
created target will be its TargetId, instead of the
feature's Name.
--dbxref Use either the first Dbxref annotation as the
primary dbxref (that goes into feature.dbxref_id),
or if an optional argument is supplied, the first
dbxref that has a database part (ie, before the ':')
that matches the supplied pattern is used.
--delete Instead of inserting features into the database,
use the GFF lines to delete features as though
the CRUD=delete-all option were set on all lines
(see 'Deletes and updates via GFF below'). The
loader will ask for confirmation before continuing.
Works like --delete except that it does not ask
for confirmation.
--fp_cv Name of the feature property controlled vocabulary
(defaults to 'feature_property').
--noaddfpcv By default, the loader adds GFF attribute types as
new feature_property cv terms when missing. This flag
deactivates it.
** dgg note: should rename this flag: --[no]autoupdate
for Chado tables cvterm, cv, db, organism, analysis ...

--manual Detailed manual pages
--custom_adapter Use a custom subclass adaptor for Bio::GMOD::DB::Adapter
Provide the path to the adapter as an argument
--private_schema Load the data into a non-public schema.
--use_public_cv When loading into a non-public schema, load any cv and
cvterm data into the public schema
--end_sql SQL code to execute after the data load is complete
Allow Parent tags to refer to IDs outside the current
GFF file

Note that all of the arguments that begin 'db' as well as organism can be provided by
default by Bio::GMOD::Config, which was installed when 'make install' was run. Also note
the the option dbprofile and all other db* options are mutually exclusive--if you supply
dbprofile, do not supply any other db* options, as they will not be used.


The GFF in the datafile must be version 3 due to its tighter control of the specification
and use of controlled vocabulary. Accordingly, the names of feature types must be exactly
those in the Sequence Ontology Feature Annotation (SOFA), not the synonyms and not the
accession numbers (SO accession numbers may be supported in future versions of this

Note that the ##sequence-region directive is not supported as a way of declaring a
reference sequence for a GFF3 file. The ##sequence-region directive is not expressive
enough to define what type of thing the sequence is (ie, is it a chromosome, a contig, an
arm, etc?). If your GFF file uses a ##sequence-region directive in this way, you must
convert it to a full GFF3 line. For example, if you have this line:

##sequence-region chrI 1 9999999

Then is should be converted to a GFF3 line like this:

chrI . chromosome 1 9999999 . . . ID=chrI

How GFF3 is stored in chado
Here is summary of how GFF3 data is stored in chado:

Column 1 (reference sequence)
The reference sequence for the feature becomes the srcfeature_id of the feature in the
featureloc table for that feature. That featureloc generally assigned a rank of zero
if there are other locations associated with this feature (for instance, for a match
feature), the other locations will be assigned featureloc.rank values greater than

Column 2 (source)
The source is stored as a dbxref. The chado instance must of an entry in the db table
named 'GFF_source'. The script will then create a dbxref entry for the feature's
source and associate it to the feature via the feature_dbxref table.

Column 3 (type)
The cvterm.cvterm_id of the SOFA type is stored in feature.type_id.

Column 4 (start)
The value of start minus 1 is stored in featureloc.fmin (one is subtracted because
chado uses interbase coordinates, whereas GFF uses base coordinates).

Column 5 (end)
The value of end is stored in featureloc.fmax.

Column 6 (score)
The score is stored in one of the score columns in the analysisfeature table. The
default is analysisfeature.significance. See the section below on analysis results
for more information.

Column 7 (strand)
The strand is stored in featureloc.strand.

Column 8 (phase)
The phase is stored in featureloc.phase. Note that there is currently a problem with
the chado schema for the case of single exons having different phases in different
transcripts. If your data has just such a case, complain to
[email protected] to find ways to address this problem.

Column 9 (group)
Here is where the magic happens.

Assigning feature.name, feature.uniquename
The values of feature.name and feature.uniquename are assigned according to these
simple rules:

If there is an ID tag, that is used as feature.uniquename
otherwise, it is assigned a uniquename that is equal to 'auto' concatenated
with the feature_id.

If there is a Name tag, it's value is set to feature.name;
otherwise it is null.

Note that these rules are much more simple than that those that Bio::DB::GFF
uses, and may need to be revisited.

Assigning feature_relationship entries
All Parent tagged features are assigned feature_relationship entries of 'part_of'
to their parent features. Derived_from tags are assigned 'derived_from'
relationships. Note that parent features must appear in the file before any
features use a Parent or Derived_from tags referring to that feature.

Alias tags
Alias values are stored in the synonym table, under both synonym.name and
synonym.synonym_sgml, and are linked to the feature via the feature_synonym table.

Dbxref tags
Dbxref values must be of the form 'db_name:accession', where db_name must have an
entry in the db table, with a value of db.name equal to 'DB:db_name'; several
database names were preinstalled with the database when 'make prepdb' was run.
Execute 'SELECT name FROM db' to find out what databases are already available.
New dbxref entries are created in the dbxref table, and dbxrefs are linked to
features via the feature_dbxref table.

Gap tags
Currently is mostly ignored--the value is stored as a featureprop, but otherwise
is not used yet.

Note tags
The values are stored as featureprop entries for the feature.

Any custom (ie, lowercase-first) tags
Custom tags are supported. If the tag doesn't already exist in the cvterm table,
it will be created. The value will stored with its associated cvterm in the
featureprop table.

When the Ontology_term tags are used, items from the Gene Ontology and Sequence
Ontology will be processed automatically when the standard DB:accession format is
used (e.g. GO:0001234). To use other ontology terms, you must specify that
mapping of the DB indentifiers in the GFF file and the name of the ontologies in
the cv table as a comma separated tag=value pairs. For example, to use plant and
cell ontology terms, you would supply on the command line:

--ontology 'PO=plant ontology,CL=cell ontology'

where 'plant ontology' and 'cell ontology' are the names in the cv table exactly
as they appear.

Target tags
Proper processing of Target tags requires that there be two source features
already available in the database, the 'primary' source feature (the chromosome or
contig) and the 'subject' from the similarity analysis, like an EST, cDNA or
syntenic chromosome. If the subject feature is not present, the loader will
attempt to create a placeholder feature object in its place. If you have a fasta
file the contains the subject, you can use the perl script, gmod_fasta2gff3.pl,
that comes with this distribution to make a GFF3 file suitable for loading into
chado before loading your analysis results.

CDS and UTR features
The way CDS features are represented in Chado is as an intersection of a
transcript's exons and the transcripts polypeptide feature. To allow proper
translation of a GFF3 file's CDS features, this loader will convert CDS and UTR
feature lines to corresponding exon features (and add a featureprop note that the
exon was inferred from a GFF3 CDS and/or UTR line), and create a polypeptide
feature that spans the genomic region from the start of translation to the stop.

If your GFF3 file contains both exon and CDS/UTR features, then you will want to
suppress the creation of the exon features and instead will only want a
polypeptide feature to be created. To do this, use the --noexon option. In this
case, the CDS and UTR features will still be converted to exon features as
described above.

Note that in the case where your GFF file contains CDS and/or UTR features that do
not belong to 'central dogma' genes (that is, that have a gene, transcript and
CDS/exon features), none of the above will happen and the features will be stored
as is.

Loading fasta file
When the --fastafile is provided with an argument that is the path to a file
containing fasta sequence, the loader will attempt to update the feature table with
the sequence provided. Note that the ID provided in the fasta description line must
exactly match what is in the feature table uniquename field. Be careful if it is
possible that the uniquename of the feature was changed to ensure uniqueness when it
was loaded from the original GFF. Also note that when loading sequence from a fasta
file, loading GFF from standard in is disabled. Sorry for any inconvenience.

This script does not use sequence-region directives for anything. If it represents a
feature that needs to be inserted into the database, it should be represented with a
full GFF line. This includes the reference sequence for the features if it is not
already in the database, like a chromosome. For example, this:

##sequence-region chr1 1 213456789

should change to this:

chr1 UCSC chromosome 1 213456789 . . . ID=chr1

This application will, by default, try to load all of the data at once as a single
transcation. This is safer from the database's point of view, since if anything bad
happens during the load, the transaction will be rolled back and the database will be
untouched. The problem occurs if there are many (say, greater than a 2-300,000) rows
in the GFF file. When that is the case, doing the load as a single transcation can
result in the machine running out of memory and killing processes. If --notranscat is
provided on the commandline, each table will be loaded as a separate transaction.

This bulk loader was originally designed to use the PostgreSQL COPY FROM syntax for
bulk loading of data. However, as mentioned in the 'Transactions' section, memory
issues can sometimes interfere with such bulk loads. In another effort to circumvent
this issue, the bulk loader has been modified to optionally create INSERT statements
instead of the COPY FROM statements. INSERT statements will load much more slowly
than COPY FROM statements, but as they load and commit individually, they are more
more likely to complete successfully. As an indication of the speed differences
involved, loading yeast GFF3 annotations (about 16K rows), it takes about 5 times
longer using INSERTs versus COPY on my laptop.

Deletes and updates via GFF
There is rudimentary support for modifying the features in an existing database via
GFF. Currently, there is only support for deleting. In order to delete, the GFF line
must have a custom tag in the ninth column, 'CRUD' (for Create, Replace, Update and
Delete) and have a recognized value. Currently the two recognized values are
CRUD=delete and CRUD=delete-all.

IMPORTANT NOTE: Using the delete operations have the potential of creating orphan
features (eg, exons whose gene has been deleted). You should be careful to make sure
that doesn't happen. Included in this distribution is a PostgreSQL trigger (written in
plpgsql) that will delete all orphan children recursively, so if a gene is deleted,
all transcripts, exons and polypeptides that belong to that gene will be deleted too.
See the file modules/sequence/functions/delete-trigger.plpgsql for more information.

The delete option will delete one and only one feature for which the name, type
and organism match what is in the GFF line with what is in the database. Note
that feature.uniquename are not considered, nor are the coordinates presented in
the GFF file. This is so that updates via GFF can be done on the coordinants. If
there is more than one feature for which the name, type and organism match, the
loader will print an error message and stop. If there are no features that match
the name, type and organism, the loader will print a warning message and continue.

The delete-all option works similarly to the delete option, except that it will
delete all features that match the name, type and organism in the GFF line (as
opposed to allowing only one feature to be deleted). If there are no features
that match, the loader will print a warning message and continue.

The run lock
The bulk loader is not a multiuser application. If two separate bulk load processes
try to load data into the database at the same time, at least one and possibly all
loads will fail. To keep this from happening, the bulk loader places a lock in the
database to prevent other gmod_bulk_load_gff3.pl processes from running at the same
time. When the application exits normally, this lock will be removed, but if it
crashes for some reason, the lock will not be removed. To remove the lock from the
command line, provide the flag --remove_lock. Note that if the loader crashed
necessitating the removal of the lock, you also may need to rebuild the uniquename
cache (see the next section).

The uniquename cache
The loader uses the chado database to create a table that caches feature_ids,
uniquenames, type_ids, and organism_ids of the features that exist in the database at
the time the load starts and the features that will be added when the load is
complete. If it is possilbe that new features have been added via some method that is
not this loader (eg, Apollo edits or loads with XORT) or if a previous load using this
loader was aborted, then you should supply the --recreate_cache option to make sure
the cache is fresh.

By default, if there is sequence in the GFF file, it will be loaded into the residues
column in the feature table row that corresponds to that feature. By supplying the
--nosequence option, the sequence will be skipped. You might want to do this if you
have very large sequences, which can be difficult to load. In this context, "very
large" means more than 200MB.

Also note that for sequences to load properly, the GFF file must have the ##FASTA
directive (it is required for proper parsing by Bio::FeatureIO), and the ID of the
feature must be exactly the same as the name of the sequence following the > in the
fasta section.

The ORGANISM table
This script assumes that the organism table is populated with information about your
organism. If you are unsure if that is the case, you can execute this command from
the psql command-line:

select * from organism;

If you do not see your organism listed, execute this command to insert it:

insert into organism (abbreviation, genus, species, common_name)
values ('H.sapiens', 'Homo','sapiens','Human');

substituting in the appropriate values for your organism.

Parents/children order
Parents must come before children in the GFF file.

If you are loading analysis results (ie, blat results, gene predictions), you should
specify the -a flag. If no arguments are supplied with the -a, then the loader will
assume that the results belong to an analysis set with a name that is the
concatenation of the source (column 2) and the method (column 3) with an underscore in
between. Otherwise, the argument provided with -a will be taken as the name of the
analysis set. Either way, the analysis set must already be in the analysis table.
The easist way to do this is to insert it directly in the psql shell:

INSERT INTO analysis (name, program, programversion)
VALUES ('genscan 2005-2-28','genscan','5.4');

There are other columns in the analysis table that are optional; see the schema
documentation and '\d analysis' in psql for more information.

Chado has four possible columns for storing the score in the GFF score column; please
use whichever is most appropriate and identifiy it with --score_col flag (significance
is the default). Note that the name of the column can be shortened to one letter. If
you have more than one score associated with each feature, you can put the other
scores in the ninth column as a tag=value pair, like 'identity=99', and the bulk
loader will put it in the featureprop table (provided there is a cvterm for identity;
see the section above concerning custom tags). Available options are:

significance (default)

A planned addtion to the functionality of handling analysis results is to allow
"mixed" GFF files, where some lines are analysis results and some are not.
Additionally, one will be able to supply lists of types (optionally with sources) and
their associated entry in the analysis table. The format will probably be tag value

--analysis match:Rice_est=rice_est_blast, \
match:Maize_cDNA=maize_cdna_blast, \

Grouping features by ID
The GFF3 specification allows features like CDSes and match_parts to be grouped
together by sharing the same ID. This loader does not support this method of
grouping. Instead the parent feature must be explicitly created before the parts and
the parts must refer to the parent with the Parent tag.

External Parent IDs
The GFF3 specification states that IDs are only valid within a single GFF file, so you
can't have Parent tags that refer to IDs in another file. By specificifying the
"allow_external_parent" flag, you can relax this restriction. A word of warning
however: if the parent feature's uniquename/ID was modified during loading (to make it
unique), this functionality won't work, as it won't beable to find the original
feature correctly. Actually, it may be worse than not working, it may attach child
features to the wrong parent. This is why it is a bad idea to use this functionality!
Please use with caution.


Allen Day <[email protected]>, Scott Cain <[email protected]>

Copyright (c) 2011

This library is free software; you can redistribute it and/or modify it under the same
terms as Perl itself.

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